We consider all 17 downstream tasks in [1]} and 2 additional sentence-pair tasks.
We group performance on datasets by the type of the task:
(1) Sentiment classification: 4 domains (Books, DVD, Kitchen, Electronics) of Amazon review binary sentiment datasets [2]};
(2) Rating classification: 4 domain of 3-way classification based on ratings of reviews from the above Amazon datasets, 1 dataset on 3-way classification of tweets about sentiment towards Airlines;
(3) Entity typing: CoNLL-2003 [3]} entity mention classification into 4 coarse types, MIT-Restaurant [4]} task on classifying mentions in user queries about restaurants into 8 types;
(4) Sentence-pair classification: Scitail, a scientific natural language inference dataset [5]}, RTE task on textual entailment and MRPC task on paraphrase classification from the GLUE benchmark [6]}.
(5) Other text classification: multiple social-media datasets on classifying tweets into (a) 2-way: political audience, bias or mention of a disaster, (b) 9-way: classifying based on political message, (c) 13-way: classifying emotion.
